Learning analytics dashboards are increasingly becoming about learning and not just analytics - A systematic review
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This systematic review explores the emerging themes in the design and implementation of student-facing learning analytics dashboards in higher education. Learning Analytics has long been criticised for focusing too much on the analytics, and not enough on the learning. The review is then guided by an interest in whether these dashboards are still primarily analytics-driven or if they have become pedagogically informed over time. By mapping the identified themes of technological maturity, informing frameworks, affordances, data sources, and analytical levels over publications per year, the review identifies an emerging trajectory towards student-focused dashboards. These dashboards are informed by theory-oriented frameworks, designed to incorporate affordances that supporting student learning, and realised through integration of more than just activity data from learning management systems – allowing the dashboards to better support students' learnings processes. Based on this emerging trajectory, the review provides a series of design recommendations for student-focused dashboards that are connected to learning sciences as well as analytics.
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Studies frequently equated engagement with observable online behaviours, such as the number of clicks or time logged in, which predominantly captures only the behavioural aspect.
“Studies frequently equated engagement with observable online behaviours, such as the number of clicks or time logged in, which predominantly captures only the behavioural aspect (Algayres & Triantafyllou, 2019;Paulsen & Lindsay, 2024).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Paulsen & Lindsay, 2024
Section 3.4 (page not specified) reports that LMS activity logs were the most common data source in the reviewed dashboards, appearing in 26 of 39. That supports frequent reliance on observable online activity, but the paper does not say that studies equated engagement with clicks or time logged in, or explicitly assess those measures as capturing only behavioural engagement. (LLM, conf=0.83)
Experimental — accuracy not yet measuredReliance on static questionnaires has been increasingly questioned, as self-reported learning styles may not accurately reflect learners' actual study behaviors in digital environments.
“However, the reliance on static questionnaires has been increasingly questioned, as selfreported learning styles may not accurately reflect learners' actual study behaviors in digital environments.”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [9]
Section 3.4 (Data sources; page not provided) distinguishes LMS activity logs from student self-reported data, but does not assess whether the two agree. Searches for static questionnaires, learning styles, and the validity of self-reports found no passage questioning whether reported styles reflect study behavior in digital environments. (LLM, conf=0.74)
Experimental — accuracy not yet measuredDiscrepancies between self-reported and behavior-derived learning styles were explicitly identified, highlighting the fluid and context-dependent nature of learner preferences.
“Importantly, discrepancies between self-reported and behavior-derived learning styles were explicitly identified, highlighting the fluid and context-dependent nature of learner preferences [8][9][10][11].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [9]
Section 3.5 lists self-reports and LMS activity as data sources, but does not compare self-reported with behavior-derived learning styles. Searches for that discrepancy and for context-dependent learner preferences found no passage in our copy addressing either point. (LLM, conf=0.76)
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- Authors
- Paulsen, L., & Lindsay, E.
- Affiliations
- Aalborg University (DK)
- Corresponding author
- Paulsen, Lucas
- Venue
- Education and Information Technologies
- Published
- 2024-01-05
- Volume
- 29
- Issue
- 11
- Pages
- 14279-14308
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4390616983
- Cited by
- 111
- References
- 69
- Citations by year
- 2026: 68 · 2025: 34 · 2024: 9
- FWCI
- 8.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Education and Information Technologies (publishedVersion, cc-by)
- VBN Forskningsportal (Aalborg Universitet) (acceptedVersion, cc-by)
- Funders
- Aalborg University
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- learning analytics dashboards, higher education, learning sciences, virtual learning environments, learning analytics framework, affordances, data sources, levels of analysis, student learning support
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